saline solution

Every Christian health professional has a unique opportunity to improve their patients' physical and spiritual health, but many feel frustrated by the challenge of integrating faith and practice within time constraints and legal obligations.

However, the medical literature increasingly recognises the important link between spirituality and health (1) and GMC guidelines approve discussion of faith issues with patients provided that it is done appropriately and sensitively (2).

Christians are called to be 'the salt of the earth' flavouring life with grace and truth.
Saline Solution
is a day course designed to help Christian healthcare professionals bring Christ into every day work. It has helped hundreds become more comfortable and adept at practising medicine that addresses the needs of the whole person.

Saline Solution
is a day course designed to help Christian healthcare professionals bring Christ into every day work. It has helped hundreds become more comfortable and adept at practising medicine that addresses the needs of the whole person.

The course is aimed at helping Christian doctors and other healthcare professionals recognise God-given opportunities to demonstrate Christian love and concern.

There has been an enthusiastic response wherever CMF has held a Saline Solution day conference. If you are interested in having a conference in your area please contact Pablo Fernandez (CMF Head of Graduate Ministries)
via our contact form.

The
Alertmanager
handles alerts sent by client applications such as the Prometheus server. It takes care of deduplicating, grouping, and routing them to the correct receiver integration such as email, PagerDuty, or OpsGenie. It also takes care of silencing and inhibition of alerts.

The following describes the core concepts the Alertmanager implements. Consult the
configuration documentation
to learn how to use them in more detail.

Grouping categorizes alerts of similar nature into a single notification. This is especially useful during larger outages when many systems fail at once and hundreds to thousands of alerts may be firing simultaneously.

Example:
Dozens or hundreds of instances of a service are running in your cluster when a network partition occurs. Half of your service instances can no longer reach the database. Alerting rules in Prometheus were configured to send an alert for each service instance if it cannot communicate with the database. As a result hundreds of alerts are sent to Alertmanager.

Example:

As a user, one only wants to get a single page while still being able to see exactly which service instances were affected. Thus one can configure Alertmanager to group alerts by their cluster and alertname so it sends a single compact notification.

Grouping of alerts, timing for the grouped notifications, and the receivers of those notifications are configured by a routing tree in the configuration file.

Inhibition is a concept of suppressing notifications for certain alerts if certain other alerts are already firing.

Example:
An alert is firing that informs that an entire cluster is not reachable. Alertmanager can be configured to mute all other alerts concerning this cluster if that particular alert is firing. This prevents notifications for hundreds or thousands of firing alerts that are unrelated to the actual issue.

Inhibitions are configured through the Alertmanager's configuration file.

Silences are a straightforward way to simply mute alerts for a given time. A silence is configured based on matchers, just like the routing tree. Incoming alerts are checked whether they match all the equality or regular expression matchers of an active silence. If they do, no notifications will be sent out for that alert.

Silences are configured in the web interface of the Alertmanager.

Quartiles are useful, but they are also somewhat limited because they do not take into account every score in our group of data. To get a more representative idea of spread we need to take into account the actual values of each score in a data set. The absolute deviation, variance and
standard deviation
are such measures.

The absolute and mean absolute deviation show the amount of deviation (variation) that occurs around the mean score. To find the total variability in our group of data, we simply add up the deviation of each score from the mean. The average deviation of a score can then be calculated by dividing this total by the number of scores. How we calculate the deviation of a score from the mean depends on our choice of statistic, whether we use absolute deviation, variance or
standard deviation
.

Perhaps the simplest way of calculating the deviation of a score from the mean is to take each score and minus the mean score. For example, the mean score for the group of 100 students we used earlier was 58.75 out of 100. Therefore, if we took a student that scored 60 out of 100, the deviation of a score from the mean is 60 - 58.75 = 1.25. It is important to note that scores above the mean have positive deviations (as demonstrated above), whilst scores below the mean will have negative deviations.

To find out the total variability in our data set, we would perform this calculation for all of the 100 students' scores. However, the problem is that because we have both positive and minus signs, when we add up all of these deviations, they cancel each other out, giving us a total deviation of zero. Since we are only interested in the deviations of the scores and not whether they are above or below the mean score, we can ignore the minus sign and take only the absolute value, giving us the
absolute deviation
. Adding up all of these absolute deviations and dividing them by the total number of scores then gives us the mean absolute deviation (see below). Therefore, for our 100 students the mean absolute deviation is 12.81, as shown below:

absolute deviation

Another method for calculating the deviation of a group of scores from the mean, such as the 100 students we used earlier, is to use the variance. Unlike the absolute deviation, which uses the absolute value of the deviation in order to "rid itself" of the negative values, the variance achieves positive values by squaring each of the deviations instead. Adding up these squared deviations gives us the sum of squares, which we can then divide by the total number of scores in our group of data (in other words, 100 because there are 100 students) to find the variance (see below). Therefore, for our 100 students, the variance is 211.89, as shown below: